{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b46bdc9",
   "metadata": {},
   "source": [
    "# python 作业\n",
    "\n",
    "## 作业要求\n",
    "\n",
    "绘制 sin 函数波形\n",
    "使用了库 numpy \n",
    "和 pylab\n",
    "\n",
    "## 代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "629ec962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pylab as pl\n",
    "\n",
    "x = np.linspace(0, 4*np.pi, 100)\n",
    "pl.plot( x, np.cos(x))\n",
    "pl.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
